Modelling and Simulation of Citric Acid Production from Corn Starch Hydrolysate Using Aspergillus Niger
Bibliographic record
Abstract
The kinetics of citric acid fermentation from corn starch hydrolysate using Aspergillus niger ATCC 9142 was studied in a batch fermenter. A general model for citric acid production was formulated. Four kinetic models, Monod, Haldane, logistic and hyperbolic for describing the growth of the fermenting microorganism were explored. The validity of the models in terms of predicting growth of the fermenting organism was determined by fitting each kinetic model to experimental data collected in the course of this work. Comparison of experimental results to model predicted results showed that only the hyperbolic model was able to accurately replicate the experimental results. This was evident from the high level of correlation between the experimental and model predicted results. The kinetic parameters for cell growth, substrate consumption and product formation µmax, Yx/s, Yp/x, Ks and Kp as calculated by the hyperbolic model are 0.01320h-1, 0.711g/g, 13.6708g/g, 0.0006g/dm3, and 0.2572 g/dm3 respectively. The validated model was implemented in an advanced equation oriented modelling software to determine the effect of key process parameters on the production of citric acid. Results of simulating the model show that the production of citric acid is a growth associated process. Optimum pH, initial sugar concentration and temperature of for citric acid production were 5.5, 40w/v and 30oC respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".